Physiology

We propose a method for removing motion artifacts from Electrodermal Activity using a stationary wavelet transform. We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level and skin conductance responses. Our method achieves a greater reduction of artifacts while retaining motion-artifact-free data.

My Master’s Thesis investigated the usefulness of different data sources for automatically predicting when students using an Intelligent Tutoring System were engaged and curious, or disengaged and bored. Detailed comparisons of machine learning algorithms trained with eye-tracking data, Electrodermal Activity (EDA) and distance from the screen revealed that distance (which can be obtained with cheap infra-red sensors) provided one of the simplest and most reliable signals of student engagement.